ID | 63231 |
フルテキストURL | |
著者 |
Toda, Yuichiro
Graduate School of Natural Science and Technology, Okayama University
Kaken ID
publons
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Wada, Akimasa
Graduate School of Natural Science and Technology, Okayama University
Miyase, Hikari
Graduate School of Natural Science and Technology, Okayama University
Ozasa, Koki
Graduate School of Natural Science and Technology, Okayama University
Matsuno, Takayuki
Graduate School of Natural Science and Technology, Okayama University
Minami, Mamoru
Graduate School of Natural Science and Technology, Okayama University
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抄録 | Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research.
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キーワード | 3D space perception
growing neural gas
topological structure learning method
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発行日 | 2022-02-07
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出版物タイトル |
Applied Sciences-Basel
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巻 | 12巻
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号 | 3号
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出版者 | MDPI
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開始ページ | 1705
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ISSN | 2076-3417
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資料タイプ |
学術雑誌論文
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言語 |
英語
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OAI-PMH Set |
岡山大学
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著作権者 | © 2022 by the authors.
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論文のバージョン | publisher
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DOI | |
Web of Science KeyUT | |
関連URL | isVersionOf https://doi.org/10.3390/app12031705
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ライセンス | https://creativecommons.org/licenses/by/4.0/
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助成機関名 |
Japan Society for the Promotion of Science
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助成番号 | 20K19894
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